International Business Machines Corporation

Yasutaka Nishimura

Shoichiro Watanabe

Takahito Tashiro

Sanehiro Furuichi

Kenichi Takasaki

Satoshi Hosokawa

Tadashi Tsumura

Mari Abe Fukuda

Kazuhito Akiyama

Yasuhisa Gotoh

Makoto Tanibayashi

Masami Tada

Hiroya Ogihara

Yukihiro Murakami

Shinkichi Hamada

Yayoi Fujiwara

Akira Ohkado

Asuka Unno

Akihiko Takajo

Makoto Kogoh

Takashi Ogura

Asuka Tokunaga

Yasuhiro Suzuki

Taku Sasaki

Taku Aratsu

Yuka Sasaki

Nobumitsu Matoba

Kaoru Hosokawa

Hirofumi Isobe

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

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

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

An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may receive an event detected in a geographic space by a mobile object of a plurality of mobile objects. The embodiment may determine a reliability of the event based on a reliability associated with the mobile object. The embodiment may store the event in a mobile object database based on the reliability of the even being above a reference reliability.

Management of mobile objects

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

The present invention may be a method, a computer system, and a computer program product for grouping a plurality moving objects capable of communicating with a server computer. The server computer performs the method comprising: predicting travel routes of the plurality of moving objects, using current traveling data and travel history data; grouping the plurality of moving objects into at least one group, using the predicted travel routes; determining a representative moving object in each group; and communicating with the representative moving object.

Grouping of moving objects

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

Disclosed embodiments provide techniques for generation of vehicle travel data. Past-travel data including previously taken trips, along with metadata including, but not limited to, vehicle type, vehicle model, navigation device model, are obtained and classified. A travel route for simulation data is selected. Data from various previous trips are combined to create a simulation data set for the travel route. This data is based on actual vehicle data. This can enable a new level of accuracy for data analysis and simulations based on this data as compared with purely simulated data. Actual vehicle data accounts for driver behavior, road obstacles, signage, vegetation, vehicle characteristics, and other factors that may not be able to be captured with purely simulated traffic data. Using the data generated by disclosed embodiments enables these factors to be considered in traffic analysis.

System and method for generating vehicle travel data

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.

Secure data navigation

Position data may gradually pseudonymized by a method, comprising: generating a sequence of relative positions from a sequence of absolute positions of a moving object; randomizing the sequence of relative positions using at least a sequence of random numbers generated from at least one seed; in response to receiving an analytical job comprising the at least one seed, restoring the sequence of relative positions from the randomized sequence of relative positions; and in response to receiving an analytical job comprising both the at least one seed and at least one absolute position derived from the sequence of absolute positions, restoring the sequence of absolute positions from the randomized sequence of relative positions.

Position data pseudonymization

A method, system, and computer program product are provided. At least some received event candidate information concerning a possible event is stored as an event candidate in a database. A dialogue is generated with an occupant of a vehicle located in a vicinity of the possible event to obtain and store information to resolve any insufficiency or ambiguity regarding the event candidate in the database. When the information stored in the database regarding the event candidate is determined to be sufficient and unambiguous the event candidate is made into an event in the database. The event is reported to at least one vehicle approaching a location corresponding to the event.

Growing community of inventors

Yamato, Japan

Yasutaka Nishimura