Abstract: A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Abstract: A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Abstract: A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Abstract: A mechanism is provided to improve the availability of an ICS and an external system that uses data from the ICS by ensuring operation of the ICS and operation of the system even if an anomaly has occurred in a device in the ICS. The mechanism receives measured data from the plurality of devices, calculates prediction data by using the measured data and correlation information used for deriving prediction data for correlated devices, and provides the measured data and the prediction data.

Abstract: A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Abstract: Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment is disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, and a model mixing module. The edge device analyzes collected data with a model for a first task, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group, transmits a request for local models to the heterogeneous group, and receives local models from the heterogeneous group. The edge device filters the local models by structure metadata, including second local models, which relate to a second task. The edge device performs a mix operation of the second local models to generate a mixed model which relates to the second task, and transmits the mixed model to the heterogeneous group.

Abstract: Systems, methods, and apparatus for time series data adaptation, including sensor fusion, are disclosed. For example, a system includes a variational inference machine, a sequential data forecast machine including a hidden state, and a machine learning model. The sequential data forecast machine exports a version of the hidden state. The variational inference machine receives as inputs time series data and the version of the hidden state, and outputs a time dependency infused latent distribution. The sequential data forecast machine obtains the version of the hidden state, receives as inputs the time series data and the time dependency infused latent distribution, and updates the hidden state based on the time series data, the time dependency infused latent distribution, and the version of the hidden state to generate a second version of the hidden state. The time dependency infused latent distribution is input into the machine learning model, which outputs a result.

Abstract: A control apparatus performs analysis by using partial information and determines whether or not communication is abnormal. If the communication is determined to be abnormal, the control apparatus controls a communication route for a communication control device such that the communication is transmitted from a communication apparatus to the control apparatus. Further, the control apparatus determines whether or not the communication transmitted by the control of the communication route is malicious communication. As a result, if the communication is determined to be malicious communication, the control apparatus controls the communication control device to restrict the malicious communication.

Abstract: Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment is disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, and a model mixing module. The edge device analyzes collected data with a model for a first task, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group, transmits a request for local models to the heterogeneous group, and receives local models from the heterogeneous group. The edge device filters the local models by structure metadata, including second local models, which relate to a second task. The edge device performs a mix operation of the second local models to generate a mixed model which relates to the second task, and transmits the mixed model to the heterogeneous group.

Abstract: A machine learning heterogeneous edge device, method, and system are disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, a group determination module, and a leader election module. The edge device analyzes collected data with a model, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group. The edge device determines group membership and determines a leader edge device. The edge device receives a request for the local model, transmits the local model to the leader edge device, receives a mixed model created by the leader edge device performing a mix operation of the local model and a different local model, and replaces the local model with the mixed model.

Abstract: A machine learning heterogeneous edge device, method, and system are disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, a group determination module, and a leader election module. The edge device analyzes collected data with a model, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group. The edge device determines group membership and determines a leader edge device. The edge device receives a request for the local model, transmits the local model to the leader edge device, receives a mixed model created by the leader edge device performing a mix operation of the local model and a different local model, and replaces the local model with the mixed model.

Abstract: A method applied to a computer that determines a situation of a system includes the steps of: receiving measurement data from each of a plurality of measurement targets in the system; computing a plurality of sets of anomaly values based on the measurement data and a predetermined computation algorithm according to a plurality of classifications corresponding to a plurality of properties of each measurement target; and determining the situation of the system based on the sets of anomaly values and a predetermined determination algorithm.

Abstract: A control apparatus performs analysis by using partial information and determines whether or not communication is abnormal. If the communication is determined to be abnormal, the control apparatus controls a communication route for a communication control device such that the communication is transmitted from a communication apparatus to the control apparatus. Further, the control apparatus determines whether or not the communication transmitted by the control of the communication route is malicious communication. As a result, if the communication is determined to be malicious communication, the control apparatus controls the communication control device to restrict the malicious communication.

Abstract: Systems, methods, and apparatus for time series data adaptation, including sensor fusion, are disclosed. For example, a system includes a variational inference machine, a sequential data forecast machine including a hidden state, and a machine learning model. The sequential data forecast machine exports a version of the hidden state. The variational inference machine receives as inputs time series data and the version of the hidden state, and outputs a time dependency infused latent distribution. The sequential data forecast machine obtains the version of the hidden state, receives as inputs the time series data and the time dependency infused latent distribution, and updates the hidden state based on the time series data, the time dependency infused latent distribution, and the version of the hidden state to generate a second version of the hidden state. The time dependency infused latent distribution is input into the machine learning model, which outputs a result.

Abstract: In a case where supervised (learning) data is prepared and the case where test data is prepared, the data is recorded with time information attached to the data. The method includes clustering the learning data in a target class and clustering the test data in the target class. Then, the probability density for each of identified subclasses is calculated for each of time intervals having various time points and widths for the learning data, and is calculated for each of time intervals in the latest time period which have various widths, for the test data. Then, a ratio between a probability density obtained when learning is performed and a probability density obtained when testing is performed is obtained as a relative frequency in each of the time intervals for each of the subclasses. Input having a relative frequency that statistically and markedly increases is detected as an anomaly.

Abstract: A machine learning heterogeneous edge device, method, and system are disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, a group determination module, and a leader election module. The edge device analyzes collected data with a model, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group. The edge device determines group membership and determines a leader edge device. The edge device receives a request for the local model, transmits the local model to the leader edge device, receives a mixed model created by the leader edge device performing a mix operation of the local model and a different local model, and replaces the local model with the mixed model.

Abstract: Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment is disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, and a model mixing module. The edge device analyzes collected data with a model for a first task, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group, transmits a request for local models to the heterogeneous group, and receives local models from the heterogeneous group. The edge device filters the local models by structure metadata, including second local models, which relate to a second task. The edge device performs a mix operation of the second local models to generate a mixed model which relates to the second task, and transmits the mixed model to the heterogeneous group.

Abstract: Provided is a system that generates models for classifying input data into a plurality of classes on the basis of training data previously classified into the plurality of classes. The system includes a sampling unit and a learning unit. The sampling unit samples, from the training data, a plurality of datasets each including a predetermined number of elements classified into a minority class and a corresponding number of elements classified into a majority class, the corresponding number being determined in accordance with the predetermined number. The learning unit learns each of a plurality of models for classifying the input data into the plurality of classes, by using a machine learning technique on the basis of each of the plurality of sampled datasets.

Abstract: A computer implemented method and system for calculating a degree of similarity between two graphs whose nodes are respectively given discrete labels include providing, for each of the two graphs, label values respectively to a given node and nodes adjacent thereto so that different ones of the discrete labels correspond to different ones of the label values. The nodes are sequentially tracing for each of the two graphs, and, during the tracing of the nodes, a new label value is calculated through a hash calculation using a label value of a currently visited node and also using label values of nodes adjacent to the currently visited node to update the label value to the currently visited node. The degree of similarity between the two graphs is calculated on the basis of the number of the label values having been given to nodes of the two graphs and agreeing between the two graphs.

Abstract: A method and an inspection apparatus for inspecting an information processing unit to which software update is applied. The apparatus includes a collection component configured to collect the behavior, for a plurality of times of software update, of the information processing unit to which one software update is applied. The apparatus also includes a determination component configured to compare the behavior collected for the plurality of times of software update to one another to determine whether the behavior of the information processing unit after the one software update is applied thereto is normal.