International Business Machines Corporation

Kenichi Takasaki

Sanehiro Furuichi

Yasutaka Nishimura

Shoichiro Watanabe

Mari Abe Fukuda

Taku Sasaki

Takuya Goto

Akira Saito

Daisuke Maruyama

Hirotaka Matsumoto

Kenta Watanabe

Yuhko Kanoh Hasegawa

Yohkichi Sasatani

Tsend Ochir Bat Ulzii

Yuka Sasaki

Junta Watanabe

Reiya Takemura

Shoichiro Wantanabe

A computer-implemented method, a computer program product, and a computer system for parallel cross validation in collaborative machine learning. A server groups local models into groups. In each group, each local device uses its local data to validate accuracies of the local models and sends a validation result to a group leader or the server. The group leader or the server selects groups whose variances of the accuracies are not below a predetermined variance threshold. In each selected group, the group leader or the server compares an accuracy of each local model with an average value of the accuracies and randomly selects one or more local models whose accuracies do not exceed a predetermined accuracy threshold. The server obtains weight parameters of selected local models and updates the global model based on the weight parameters.

Parallel cross validation in collaborative machine learning

A method for calculating traffic flow changes includes detecting a traffic event. The method further includes determining an affected area of the traffic event and determining an investigation area based on the affected area. The method further includes selecting at least one vehicle located within the investigation area and calculating a change in traffic flow due to the traffic event based on a comparison of a predicted traffic flow with a current traffic flow, wherein the current traffic flow is based on information received from the at least one vehicle. The method further includes updating the affected area based on the change in traffic flow and calculating an updated change in traffic flow based on the updated affected area when the updated affected area is larger than a predetermined threshold area.

Calculating traffic flow changes due to traffic events

Aspects of the invention include receiving an identity of a primary web-based material from a user, wherein the primary web-based material describes a topic. Subparts of the primary web-based material are indexed. Supplemental web-based materials accessed by a plurality of reference users are retrieved, wherein the supplemental web-based materials relate to the subparts. A sequence of supplemental web-based materials is generated for the user to follow to gain an understanding of the topic.

Efficiently manage and share resources during e-learning

A computer system trains a federated learning model. A federated learning model is distributed to a plurality of computing nodes, each having a set of local training data comprising labeled data samples. Statistical data is received from each computing node that indicates the node's count of data samples for each label, and is analyzed to identify one or more computing nodes having local training data in which a label category is underrepresented beyond a threshold value with respect to data samples. Additional data samples labeled with the underrepresented labels are provided, and the computing nodes perform training. Results of training are received and are processed to generate a trained global model. Embodiments of the present invention further include a method and program product for training a federated learning model in substantially the same manner described above.

Training federated learning models

Provided are a computer program product, system, and method for configuring and controlling an automated vehicle to perform user specified operations. User vehicle control programs are loaded in an unmanned vehicle to control the unmanned vehicle to perform a user specified operation. The loading the user vehicle control programs replaces base vehicle control programs in the unmanned vehicle. There is communication with the unmanned vehicle to execute the user vehicle control programs to control the unmanned vehicle to perform the user specified operation. The base vehicle control programs are loaded into the unmanned vehicle to replace the user vehicle control programs to return control of the unmanned vehicle to a vehicle provider after performing the user specified operation.

Configuring and controlling an automated vehicle to perform user specified operations

A processor may generate, based on a predicted route of a user, a timeslot for presenting information to the user. The predicted route may be associated with a route segment, and the timeslot may be associated with the route segment. The processor may match one or more proposals to the timeslot associated with the route segment. The one or more proposals may be matched, at least in part, based on proposal criteria set by a proposal criteria user associated with the proposal. The processor may predict an acceptance potentiality of the user. The processor may select a subset of the one or more proposals. The processor may provide the selected subset of the one or more proposals. The selected subset may be provided, at least in part, based on the acceptance potentiality.

Route-based information slotting based on predicted acceptance

Utilizing a trained generative adversarial network (GAN) model to cause a computer to output multivariate forecasted time-series data by providing a trained GAN model, the GAN model comprising dilated convolutional layers for receiving time-series multivariate data, receiving time-series multivariable biometric data, generating, using the GAN model, successive time series multivariate biometric data according to the time-series multivariate biometric data, determining an outcome according to the successive time-series multivariate biometric data, and providing an output associated with the outcome.

Forecasting multivariate time series data

A method, computer system, and a computer program product for preemptive collision mitigation is provided. The present invention may include calculating a future position of a first vehicle based on carprobe data from a first vehicle, wherein the carprobe data contains neural data of an operator of the first vehicle. The present invention may also include calculating a distance between the future position of the first vehicle and a future position of a second vehicle. The present invention may then include determining the calculated distance between the future position of the first vehicle and the future position of the second vehicle is below a threshold distance.

Preemptive mitigation of collision risk

Described are techniques for acquiring geospatial data according to an information value. The techniques including determining a context for geospatial data to be used in an application, where the context is based on one or more external factors that influence variation of the geospatial data. The techniques further include calculating an information value of the geospatial data in the context for each of a plurality of information acquisition methods, where the plurality of information acquisition methods include respective data acquisition frequencies and respective spatial resolutions, and where the information value is based on an information loss function, an information amount, and a cost. The techniques further include selecting a first information acquisition method with a highest information value and acquiring the geospatial data using the first information acquisition method.

Geospatial data acquisition based on information value

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises in response to receiving a data packet from a computing device, classifying the data packet as a task having one or more portions; allocating the classified task to a processing location within a data region based on a location of the computing device; in response to a change associated with the task, dynamically calculating alternate processing locations within a radius of the data region to process one or more portions of the task based on scoring values associated with the change; and redistributing at least one portion of the classified task according to an alternate processing location of dynamically calculated alternate processing locations.

Dispatching tasks and data using multi-access edge computing

A computer system provides navigation services. Route data is processed to extract a plurality of routes, wherein each route comprises a plurality of links and nodes. A partition layer is generated for a map including the plurality of links and nodes of the routes, wherein the partition layer comprises a plurality of meshes each containing links and nodes of the map. A context is processed to remove invalid links and nodes from the map based on the context. The partition layer is updated by merging a subset of the plurality of meshes in response to the removal of the invalid links and nodes. A navigation service is provided for the plurality of routes based on the updated partition layer of the map. Embodiments of the present invention further include a method and program product for providing navigation services in substantially the same manner described above.

Providing navigation services using context-aware trajectory analysis

A method, computer system, and a computer program product for supplementing learning data to determine a Most Probable Path (MPP) for a user driver is provided. The present invention may include determining a change associated with a set of map data. The present invention may then include extracting a set of trajectory data in response to the determined change associated with the set of map data. The present invention may also include performing a rerouting calculation for the extracted set of trajectory data, wherein the performed rerouting calculation generates a rerouting result, wherein the generated rerouting result is added to a set of learning data in a learning data database. The present invention may further include adding one or more trajectory patterns to the learning data based on an increase frequency after the determined change associated with the set of map data.

Supplementing learning data to determine most probable path

A method of calculating a most probable path (MPP) comprising: with a classification module, classifying each of a plurality of origin and destination (O/D) trajectories into one of a plurality of periods of time, the classification comprising: changing a data sampling time period of any of the plurality of trajectories within the determined classification periods; with the classification module, detecting any O/D data descriptive of an aperiodic O/D trajectory using context data descriptive of the aperiodic O/D trajectory; and with the classification module, calculating a route trajectory serving as a candidate of the MPP to determine a priority O/D trajectory that matches the context data.

Calculating a most probable path

A method, system and computer program product for presenting content (e.g., advertisements) to vehicle occupants. An image of the occupants of the vehicle is obtained. The vehicle occupants, such as the driver, are then identified from the image. After obtaining the driving history of the driver, which includes any information that indicates a risk posed to the driver in not paying attention to the road, a risk degree of the driver getting into an accident from viewing content on the electronic billboard is assessed. A 'watch ability' of the vehicle occupants to watch content is calculated, based, at least in part, on the driver's risk degree. If the value of the watch ability exceeds a threshold value, then the content preferences of the vehicle occupants are obtained which are used to determine the contents to be presented on the electronic billboard which are targeted to the vehicle occupants.

Presenting targeted content to vehicle occupants on electronic billboards

The method, computer program product and computer system may include computing device which may determine a destination route. The computing device may receive current travel data associated with a primary user on the destination route and determine past travel data associated with a primary user on the destination route. The computing device may calculate, in response to determining that past travel data associated with a primary user on the destination route exists, a reliability degree of the current travel data associated with a primary user on the destination route based on the past travel data associated with a primary user on the destination route. The computing device may transmit a signal to a user device, the signal providing an instruction to display features according to an abstraction level of points of interest on the destination route based on the reliability degree.

Growing community of inventors

Tokyo, Japan

Kenichi Takasaki